## Hessian Affine + SIFT keypoints in Python

This is an implementation of Hessian-Affine detector.

The implementation uses a Lowe's (Lowe 1999, Lowe 2004) like pyramid to sample Gaussian scale-space and localizes local extrema of the Detetminant of Hessian Matrix operator computed on normalized derivatives. Then a Baumberg-Lindeberg discovery of a local affine shape is employed (Lindeberg 1998, Baumberg 2000, Mikolajzyk 2002) to compute affine shape of each det of Hessian extrema. Finally a local neighbourhood is normalized to a fixed size patch and SIFT descriptor(Lowe 1999, Lowe 2004) computed.

### BUILDING

There are wheels publishe on pypi using cibuildwheel.

### IMPLEMENTATION

Implementation depends on OpenCV (2.3.1+). Although, the code is original, the affine iteration and normalization was derived from the code of Krystian Mikolajczyk.

The SIFT descriptor code was patented under a US Patent 6,711,293, which expired on March 7th 2019, so the license is no longer required for use.

### OUTPUT

NOTE THIS IS NO LONGER THE CASE. WE MAY REINSTATE THIS.

The built binary rewrites output file: <input_image_name>.hesaff.sift

The output format is compatible with the binaries available from the page "Affine Covariant Features". The geometry of an affine region is specified by: u,v,a,b,c in a(x-u)(x-u)+2b(x-u)(y-v)+c(y-v)(y-v)=1. The top left corner of the image is at (u,v)=(0,0). The geometry of an affine region is followed by N descriptor values (N = 128).

File format:

N m u1 v1 a1 b1 c1 d1(1) d1(2) d1(3) ... d1(N) : : um vm am bm cm dm(1) dm(2) dm(3) ... dm(N)

### PROPER USE

If you use this code, please refer to

Perdoch, M. and Chum, O. and Matas, J.: Efficient Representation of Local Geometry for Large Scale Object Retrieval. In proceedings of CVPR09. June 2009.

TBD: A reference to technical report describing the details and some retrieval results will be placed here.

### NOTES

Requires opencv. On ubuntu you can: `sudo apt-get install libopencv-dev`

. You can also build / use wheels.